iosr-JCE   Volume-16 ~ Issue-6 ~ Nov-Dec 2014

Abstract: The focus of this paper is towards developing an application specific design methodology for low power solutions in recursive coding system in which filtration process is performed by using MAC approach. For the realization of this MAC operation a recursive multiplication and addition operation is carried out. Theconventional recursion results in multiple accumulation operation in successive clocks of duration amounting to that of an addition-time, so that the accumulation of 𝑁 successive input words are performed in 𝑁clock cycles. For large values of 𝑁, a computational latency of 𝑁addition-time over head and high power consumption and accumulation process to obtain an accumulated output could be too high to meet the timing requirement in real-time application. so in order to over this problem in this paper a new scalable Repetitive multiply accumulates (MACs) is proposed.From the simulation results the proposed MAC has less delay complexity and low power consumption when compared to the conventional approaches.

Keyword: Recursive coding, VLSI, Application specific integrated circuits, digital signal processing chips , digital arithmetic.

[1] Kuroda, T.; Hamada, M. "Low-powerCMOS digital design with dual embedded adaptive power supplies " IEEE journal of solid-state circuits, vol. 35, no. 4, april 2000.
[2] Jan Rabaey,Jan M. Rabaey, AnanthaChandrakasan, BorivojeNikolic "Digital Integrated Circuits" (2nd Edition) Prentice Hall; 2 edition, 2003.

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[4] C. Piguet et al., "Low Power Design of 8-b Embedded CoolRISC Microcontroller Cores, "IEEE Journal of Solid-State Circuits, vol. 32, no. 7, July 1997, pp.1067-1078.

[5] Naehjuck Chang, Kuranho Kim and H. G. Lee, "Cycle accurate Energy consumption measurement and analysis: Case study ARM7 TDM1," International Symposium on Low Power Electronics and Design, Italy, July 2000, pp. 185-190.

Abstract: Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. We try to exploit computational power from the multicore processors. We need a new design on existing algorithms and software. Firefly algorithm is one of the metaheuristic algorithms which are used for solving optimization problems. The existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into picture to provide crucial contribution towards clustering large data. In this paper, we have developed a scalable parallel clustering algorithm using FA and genetic algorithm to cluster large data. Modified FA algorithm does not handle the large data effectively. So, our ultimate aim is to design and develops an algorithm in parallel way by considering data. The experimental analysis will be carried out to evaluate the feasibility of the new combined clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time. Most of the programming languages doesn't provide multiprocessing facilities and hence wastage of processing resources. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance.

Keywords: Parallel clustering, large data, Firefly, genetic algorithm.

[1] X. S. Yang, "Nature-Inspired metaheuristic Algorithms". Luniver Press, 2008.

[2] X. S. Yang, "Firefly algorithm، stochastic Test Functions and Design optimization".Int. J. bio-inspired computation .2010.

[3] X. S. Yang, "Firefly algorithm for multimodal optimization." In: Stochastic Algorithms: foundations and applications ،SAGA ،lecture notes in computer sciences, pp. 169-178, 2009.

[4] S. M. Elsayed, R. A. Sarker, and D. L. Essam, "A new genetic algorithm for solving optimization problems," Engineering Applications of Artificial Intelligence, vol. 27, pp. 57-69, 2014.
[5]. Doug Lea, A Java Fork/Join Framework, State University of New York at Oswego,www.developer.com [6] Kennedy J., Eberhart R., Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.

Abstract: Wireless communication between mobile users is becoming more popular than ever before. This is due to recent technological advances in laptop computers and wireless data communication devices, such as wireless modems and wireless LANs. This has lead to lower prices and higher data rates, which are the two main reasons why mobile computing continues to enjoy rapid growth. With current advances in technology, wireless networks are increasing in popularity. Wireless networks allow users the freedom to travel from one location to another without interruption of their computing services. However, wireless networks require the existence of a wired base station (BS) in order for the wireless user to send/receive messages. Ad-hoc networks, a subset of wireless networks, allow the formation of a wireless network without the need for a BS. All participating users in an Ad-hoc network agree to accept and forward messages, to and from each other. With this flexibility, wireless networks have the ability to form anywhere, at any time, as long as two or more wireless users are willing to communicate. This chapter will introduce the mobile ad hoc networking in general, provide background on the nature and problems of this type of networking and give an overview of the current state of research. Mobile networking is one of the most important technologies supporting pervasive computing. During the last decade, advances in both hardware and software techniques have resulted in mobile hosts and wireless networking common and miscellaneous. Generally there are two distinct approaches for enabling wireless mobile units to communicate with each other.

Keywords: Mobile ad hoc networks (MANETS) , Quality of services (QoS) , AODV, ZRP, DSDV routing

[1]. David B. Johnson and David A.Maltz, "Dynamic source routing in ad hoc wireless networks". In Mobile Computing, edited by Tomasz Imielinski and Hank Korth, chapter 5, pages 153-181. Kluwer Academic Publishers.
[2]. G. Pei, M. Gerla and T.-W. Chen, (Apr. 2000, pp. D71-D78) Fisheye State Routing in Mobile Ad Hoc Networks. In Proceedings of the 2000 ICDCS Workshops, Taipei,Taiwan,
[3]. Josh Broch et al. (1998), "A Performance Comparison of Multi Hop Wireless Adhoc Network Routing Protocols‟, Proceedings of IEEE/ACM Conference on Mobile Computing and Networking, pp. 85-97.
[4]. M. Joa-Ng and I-Tai Lu (1415 1425, 1999) A peer-to-peer zone-based two-level link state routing for mobile ad hoc net-works, IEEE on Selected Areas in Communications, vol. 17, no. 8, pp.
[5]. Neeraj Nehra, R.B. Patel, V.K. Bhat, 2007"Trust Aware Rouitng with Load Balancing in Ad Hoc Network Using Mobile Agent," Advanced Computing and Communications, International Conference on, pp. 454-459, 15th International Conference on Advanced Computing and Communications (ADCOM 2007),. Network Simulator, NS-2, The VINT Project, available from

Abstract: The use of Android in the smartphone operating system currently used by many companies. Because of its superiority as a software that uses computer code base that can be distributed openly (open source) so many applications that can be downloaded by penggguna smartphone without paying the application fee. Believed to be a smartphone that uses the Android operating system will be less costly than a smartphone that uses the operating system are paid. Then to work the MSME sector businesses is in accordance with the condition of our society, especially people Malang. It only takes a willingness Human Resources who are committed to the implementation of SME businesses and to optimize service thoroughly and consistently.

Keywords: Modern Computer Implementation, Services, SME, Android

[1]. Android.2012. http://id.wikipedia.org/wiki /Android_ (sistem_operasi). Diunduh: 19 Juli 2013, Pukul 14.23
[2]. Bagus Handoko, Haryo. 2009. Buku Tempat Makan Makanan Favorit di Malang. Malang: Gramedia.
[3]. Haryanto, Agus. 2013. Belajar Android Menampilkan Map dengan Android Google Maps API V2. http://agusharyanto.net. Diunduh: 24 Agustus 2013.
[4]. Huda, Arif Akbar. 2012. 24 JAM !!! Pintar Pemrograman Android. Yogjakarta: Andi Offset
[5]. Michael Siregar, Ivan. 2011. Membongkar Source Code berbagai Aplikasi Android. Bandung: Gava Media.
[6]. Safaat H, Nazruddin. 2012. Pemrograman Aplikasi Mobile Smartphone dan Tablet PC Berbasis Android (edisi revisi). Bandung: Informatika.

Abstract: Fingerprint authentication system has been widely emerged in authentication purpose in public as well as in private organizations. Normally fingerprint image processing is done in form of acquiring 2-D images, by using Automatic Fingerprint Identification System. The captured 2D texture will contain distortions. While comparing the enrolled distorted image from database with that of identification image, the results will be leads to false acceptance rate and false rejection rate. This paper deals with capturing of 3D fingerprint image and processed to attain improvisation in the resultant fingerprint image. The proposed scan system is based on real-time capturing by using low 3D sensor and with structured light illumination. This results in the generation of detailed in-depth ridge information and the texture of the finger in the form of 3D image. Thus generated 3D image is then converted to unraveled equal 2D images by using the proposed best fit sphere algorithm. The NIST matching software is employed to analyze and evaluate the image quality. The comparison is made between the unraveled converted 2D image and with its 2D ink-rolled data. The NIST matching software is then applied for 2D unraveled equivalent fingerprint images and results are analyzed. As a result, the fingerprints quality is improvised.

Key words: 2D unraveled image, Best fit algorithm.

[1] Veeraganesh Yalla. Optimal Phase Measuring Profilometry Techniques for Static and Dynamic 3D Data Acquisition. PhD Dissertation, University of Kentucky, Lexington, USA. July, 2006.
[2] Abhishika Fatehpuria. Performance Evaluation of Non-contact 3D Fingerprint Scanner. MS Thesis, University of Kentucky, Lexington, USA. November, 2006.
[3] Veeraganesh Yalla. Multi-frequency Phase Measuring Profilomety. MS Thesis, University of Kentucky, Lexington, USA. December 2004.
[4] C. Guan. Composite Pattern for Single Frame 3D Acquisition. PhD Dissertation, University of Kentucky, Lexington, USA. December 2004.
[5] C Guan, L. G. Hassebrook and D. L. Lau. "Composite Structure Light Pattern for Three Dimensional Video". Opt. Exp., 11, 406-417, 2003.

Abstract: In this paper a new hotel revenue management model is proposed to provide hotel managers with a flexible and efficient decision support tool for room revenue maximization. The proposed system is composed of an advanced room demand forecast model and an optimization model that takes group reservations into account. The system also allows for expert intervention to update system parameters and outputs in order to integrate domain knowledge with computer models. A case study based on three different sets of reservation records of simulated hotel data demonstrated the viability of the system for giving useful recommendations related to accepting room requests in order to maximize hotel room revenue.

Keywords: Revenue Management System, Hotel Room Demand Forecasting, Optimal Hotel Room Allocation

[1]. Abdel Aziz, H. & Saleh, M. &El Gayar, N. & El -Shishiny, H. (2008)A Randomized Model for Group Reservations in Hotel's Revenue Management System. Proceedings of INFOS'2008, March 27-29, pp. 24-30.
[2]. Ahmed, N. & Atiya, A. & El Gayar, N. & El-Shishiny, H. (2009). An Empirical Comparison of Machine Learning Models for Time Series Forecasting", accepted for publication, to appear in Econometric Reviews.
[3]. Baker, T.K. & Collier, D.A. (2003). The benefits of optimizing prices to manage demand in hotel revenue management systems. Production and Operations Management, Vol. 12, pp. 502–518.

[4]. Bertsimas, D. & de Boer, S. (2005) . Simulation-based booking limits for airline revenue Management Operations Research, Vol. 53, pp.90–106.
[5]. Brumelle, S. & Walczak, D. (2003). Dynamic airline revenue management with multiple semi-Markov demand. Operations Research. Vol. 51, pp.137–148.

Abstract: The association rule mining is most popular and real time applicable approach for finding interesting relations between items. Many of the ARM (Association rule Mining) approaches are well investigated in the literature, but it generates large number of association rules. If the dataset size is larger then huge rules may occur, often it is a critical situation where decision making is difficult or unattainable because knowledge is not directly present in frequent patterns. This paper presents an improved AIRM (Association Inference Rule Mining) algorithm where fuzzy logic based C-Means clustering concept has been adopted to discover inference knowledge from frequent patterns. For experimental study, we apply this approach on a clinical dataset of 1000 patients, contained symptoms having different diseases. Proposed approach follows three phase procedure in order to achieve inference knowledge, in the first phase preprocess the data, second phase apply the ARM and finally FIS has to be applied to discover inference knowledge by matching inference rules and put the data in the appropriate class on the basis of their matching degree. The new approach is efficient and outperforms as compared to a previous AIRM algorithm in order to match inference rules and knowledge discovery process.
Keywords: ARM, Inference rule mining, Fuzzy Symptoms, Fuzzy inference, Forward Channing.

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Santiago, Chile, pp.487–499, 1994.
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[3]. Alcala-Fdez J., Alcala R. and Herrera, F., "A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems
With Genetic Rule Selection and Lateral Tuning," Fuzzy Systems, IEEE Transactions on , vol.19, no.5, pp.857-872, 2011.
[4]. Bezdek J.C., ―Pattern Recognition with Fuzzy Objective Function Algorithms‖, Plenum Press, New York , 1981 .
[5]. Chaturvedi K., Patel R. and Swami D.K., ―An Inference Mechanism Framework for Association Rule Mining‖ International Journal
of Advanced Research in Artificial Intelligence (IJARAI),3(9), pp.1-8, 2014.

Abstract: With the advent of new and improved technology in the market, it has become extremely important to gather information and create user friendly applications. Be it for security purposes or tracking purposes, monitoring human activities to create a human gesture-friendly interface is now a major branch of research. We hope to present one such method to observe and interpret human activities. This paper describes methods to obtain human activity records (in the form of images or videos) and perform suitable actions on these records to extract useful information out of them. Background subtraction is the initial one of the most important procedures performed on the image or video. Next comes preprocessing the record to improve quality. Then finally comes Skeletonisation. We focus on Background Subtraction method using the image pixels and Skeletonisation in this paper and provide the basic steps and strategies regarding it.

Keywords: Background Subtraction, Human activity recognition, Image Pixels, Pre-processing, Skeletonization

[1] V. Vishwakarma, C. Mandal and S. Sural , Automatic Detection of Human Fall in Video, Proceeding PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence, pp. 616-623.
[2] Wadley, V., Okonkwo, O., Crowe, M., Ross-Meadows, L. A. 2007, Mild Cognitive Impairment and everyday function: Evidence of reduced speed in performing instrumental activities of daily living. American Journal of Geriatric Psychiatry, 16(5): 416-424.
[3] L. Chen, C. Nugent, J. Biswas, J. Hoey, Activity Recognition in Pervasive Intelligent Environments, Chapter 1 , Activity Recognition: Approaches, Practices and Trends, ATLANTIS PRESS, 2011, 1875-7669
[4] M. Piccardi, Background subtraction techniques: a review,2004 IEEE International Conference on Systems, Man and Cybernetics, vol.4, 2004, pp. 3099- 3104.
[5] Sungkuk Chun, Kwangjin Hong, and Keechul Jung, 3D Star Skeleton for Fast Human Posture Representation,. International Science Index, World Academy of Science, Engineering and Technology Vol:2 2008-08-27